On the ImageNet classification dataset, we present the results of additional data-free compression methods as shown in Table REF . The comparison is mainly for data-free quantization except that the GDFQ and our methods apply knowledge distillation on the output layer. None of the compared methods apply knowledge distillation on the intermediate layers. The results of DFQ [1]} and ZeroQ [2]} are cited from the GDFQ paper and have a rather low accuracy for ultra-low precision data-free quantization. The DFC [3]} method achieves a moderate accuracy with a combination of BN-Statistics and Inception schemes. Our method achieves better accuracy compared to the GDFQ method since the AutoReCon method discovers an optimized generator architecture for reconstruction.
